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@ARTICLE{on_mining_logs,
  author = {Joshi, Anupam and Krishnapuram, Raghu},
  title = {On Mining Web Access Logs},
  year = {2000},
  pages = {63--69},
  abstract = {The proliferation of information on the world wide web has made the
	personalization of this information
	
	space a necessity. One possible approach to web personalization is
	to mine typical user profiles
	
	from the vast amount of historical data stored in access logs. In
	the absence of any a priori knowledge,
	
	unsupervised classification or clustering methods seem to be ideally
	suited to analyze the semi-structured
	
	log data of user accesses. In this paper, we define the notion of
	a \&quot;user...},
  booktitle = {ACM {SIGMOD} Workshop on Research Issues in Data Mining and Knowledge
	Discovery},
  citeulike-article-id = {2272312},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.5606},
  keywords = {clustering, fuzzy, web-mining},
  posted-at = {2008-01-22 08:15:12},
  priority = {5},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.5606}
}

@MISC{ref_clf,
  author = {Wikipedia},
  title = {Common Log Format},
  year = {2009},
  note = {\url{http://en.wikipedia.org/wiki/Common_Log_Format} [Online; accessed
	21-December-2009]},
  url = {\url{http://en.wikipedia.org/w/index.php?title=Beta_distribution&oldid=275189376}}
}

@inproceedings{seq_apriori,
 author = {Bodon, Ferenc},
 title = {A trie-based APRIORI implementation for mining frequent item sequences},
 booktitle = {OSDM '05: Proceedings of the 1st international workshop on open source data mining},
 year = {2005},
 isbn = {1-59593-210-0},
 pages = {56--65},
 location = {Chicago, Illinois},
 doi = {http://doi.acm.org/10.1145/1133905.1133913},
 publisher = {ACM},
 address = {New York, NY, USA},
 }
@article{path_patterns,
 author = {Chen, Ming-Syan and Park, Jong Soo and Yu, Philip S.},
 title = {Efficient Data Mining for Path Traversal Patterns},
 journal = {IEEE Trans. on Knowl. and Data Eng.},
 volume = {10},
 number = {2},
 year = {1998},
 issn = {1041-4347},
 pages = {209--221},
 doi = {http://dx.doi.org/10.1109/69.683753},
 publisher = {IEEE Educational Activities Department},
 address = {Piscataway, NJ, USA},
 }
@article{frequent_item_set_mining,
    abstract = {this paper we propose algorithms for generation of frequent itemsets by

successive construction of the nodes of a lexicographic tree of itemsets. We

discuss different strategies in generation and traversal of the lexicographic

tree such as breadth-first search, depth-first search or a combination of

the two. These techniques provide different trade-offs in terms of the I/O,

memory and computational time requirements. We use the hierarchical

structure of the lexicographic tree to...},
    author = {Agarwal, Ramesh C. and Aggarwal, Charu C. and Prasad, V. V. V.},
    citeulike-article-id = {1379169},
    citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.9853},
    journal = {Journal of Parallel and Distributed Computing},
    number = {3},
    pages = {350--371},
    posted-at = {2007-06-11 19:43:31},
    title = {A Tree Projection Algorithm for Generation of Frequent Item Sets},
    url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.9853},
    volume = {61},
    year = {2001}
}

@inproceedings{apriori,
    abstract = {We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.},
    address = {Washington, D.C.},
    author = {Agrawal, Rakesh and Imielinski, Tomasz and Swami, Arun N.},
    booktitle = {Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data},
    citeulike-article-id = {3564},
    citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.6984},
    editor = {Buneman, Peter and Jajodia, Sushil},
    keywords = {apriori},
    month = {FebruaryJune--FebruaryAugust\~{}},
    pages = {207--216},
    posted-at = {2008-03-14 13:13:23},
    priority = {1},
    title = {Mining Association Rules between Sets of Items in Large Databases},
    url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.6984},
    year = {1993}
}

	

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